R Shiny-找不到函数 min_var 和 max_sr (renderHighchart) 最小差异投资组合权重相切组合权重投资组合优化和高效前沿Symulacje最小值、最大值、中值 Sim直方图密度滚动排序

如何解决R Shiny-找不到函数 min_var 和 max_sr (renderHighchart) 最小差异投资组合权重相切组合权重投资组合优化和高效前沿Symulacje最小值、最大值、中值 Sim直方图密度滚动排序

你能帮我找出我的 R-shiny 代码中的问题吗？ 我粘贴了整个 R-markdown 脚本，它适用于第二个选项卡“输出”，但是对于第一个名为“Portfel efektywny”的功能，存在无法找到的问题，但它已正确实现...... 警告：min_var 中的错误：找不到函数“min_var” 警告：max_sr 中的错误：找不到函数“max_sr”

我将非常感谢您的帮助:) 提前谢谢！！！

---
title: "Monte_Carlo_Projekt"
runtime: shiny
output:
  flexdashboard::flex_dashboard:
    orientation: rows
    
---

library(shiny)
library(tidyverse)
library(highcharter)
library(tidyquant)
library(plotly)
library(timetk)
library(scales)

侧边栏{.sidebar}

fluidRow(
  column(6,textInput("stock1","Stock 1","SPY")),column(5,numericInput("w1","Portf. %",25,min = 1,max = 100))
)  

fluidRow(
  column(6,textInput("stock2","Stock 2","EFA")),numericInput("w2",max = 100))
)

fluidRow(
  column(6,textInput("stock3","Stock 3","IJS")),numericInput("w3",20,textInput("stock4","Stock 4","EEM")),numericInput("w4",textInput("stock5","Stock 5","AGG")),numericInput("w5",10,max = 100))
)

fluidRow(
  column(7,dateInput("date","Starting Date","2013-01-01",format = "yyyy-mm-dd"))
)

fluidRow(
  column(5,numericInput("sim_months","Months",120,min = 6,max = 240,step = 6)),numericInput("sims","Sims",51,min = 31,max = 101,step = 10)),numericInput("mar","MAR%",.8,min = 0,max = 3,step = .01)),numericInput("window","Window",6,min = 2,max = 24,step = 2))
)

actionButton("go","Submit")

#Tu dodajemy wszystkie funkcje
prices <- eventReactive(input$go,{
  
  symbols <- c(input$stock1,input$stock2,input$stock3,input$stock4,input$stock5)
  getSymbols(symbols,src = 'yahoo',from = input$date,auto.assign = TRUE,warnings = FALSE) %>% 
  map(~Ad(get(.))) %>% 
  reduce(merge) %>%
  `colnames<-`(symbols)
})

mar <- eventReactive(input$go,{input$mar/100})

rolling_sortino <- eventReactive(input$go,{
  validate(need(input$w1 + input$w2 + input$w3 + input$w4 + input$w5 == 100,"The portfolio weights must sum to 100%!"))
  
  prices <- prices()
  w <- c(input$w1/100,input$w2/100,input$w3/100,input$w4/100,input$w5/100)
  MAR <- mar()
  window <- input$window

prices_monthly <- to.monthly(prices,indexAt = "last",OHLC = FALSE)
asset_returns_xts <- na.omit(Return.calculate(prices_monthly,method = "log"))

cov_mat <- cov(asset_returns_xts) * 12

mean_ret <- colMeans(asset_returns_xts)

portfolio_returns_xts_rebalanced_monthly <- Return.portfolio(asset_returns_xts,weights = w,rebalance_on = "months")

rolling_sortino <-
  apply.rolling(portfolio_returns_xts_rebalanced_monthly,window,fun = 'SortinoRatio',MAR = MAR) %>% 
  `colnames<-`(paste(window,"-month rolling sortino",sep=""))
})
help("apply.rolling")

portfolio_returns_tq_rebalanced_monthly <- eventReactive(input$go,{
validate(need(input$w1 + input$w2 + input$w3 + input$w4 + input$w5 == 100,"The portfolio weights must sum to 100%!"))
  prices <- prices()
  w <- c(input$w1/100,input$w5/100)
  
  asset_returns_long <- 
      prices %>% 
      to.monthly(indexAt = "last",OHLC = FALSE) %>% 
      tk_tbl(preserve_index = TRUE,rename_index = "date") %>%
      gather(asset,returns,-date) %>% 
      group_by(asset) %>%  
      mutate(returns = (log(returns) - log(lag(returns))))
  
  sims <- eventReactive(input$go,{input$sims})
  
  sims <- sims()

# Creating a matrix to store the weights
all_wts <- matrix(nrow = sims,ncol = length(symbols))

# Creating an empty vector to store Portfolio returns,Portfolio Standard deviation,Portfolio Sharpe Ratio
port_returns <- vector('numeric',length = sims)
port_risk <- vector('numeric',length = sims)
sharpe_ratio <- vector('numeric',length = sims)

for (i in seq_along(port_returns)) {
  
  wts <- runif(length(symbols))
  wts <- wts/sum(wts)

  all_wts[i,] <- wts # Storing weight in the matrix
  
  port_ret <- sum(wts * mean_ret) # Portfolio returns
  port_ret <- ((port_ret + 1)^12) - 1
  
  port_returns[i] <- port_ret # Storing Portfolio Returns values
  
  port_sd <- sqrt(t(wts) %*% (cov_mat  %*% wts)) # Creating and storing portfolio risk
  port_risk[i] <- port_sd
  
  sr <- port_ret/port_sd # Creating and storing Portfolio Sharpe Ratios,sharpe_ratio[i] <- sr #Assuming 0% Risk free rate
  
}

# Storing the values in the table
portfolio_values <- tibble(Return = port_returns,Risk = port_risk,SharpeRatio = sharpe_ratio)

# Converting matrix to a tibble and changing column names
all_wts <- tk_tbl(all_wts)

colnames(all_wts) <- colnames(asset_returns_xts)

# Combing all the values together
portfolio_values <- tk_tbl(cbind(all_wts,portfolio_values))

# calculating the minimum variance portfolio

min_var <- eventReactive(input$go,{ 
portfolio_values[which.min(portfolio_values$Risk),]
  
})

# calculating the tangency portfolio (the portfolio with highest sharpe ratio)
max_sr <- eventReactive(input$go,{ 
  portfolio_values[which.max(portfolio_values$SharpeRatio),]

})

MAR <- mar()

portfolio_returns_tq_rebalanced_monthly <- 
  asset_returns_long %>% 
  tq_portfolio(assets_col = asset,returns_col = returns,col_rename = "returns",rebalance_on = "months") %>% 
  mutate(returns_below_MAR = ifelse(returns < MAR,NA)) %>%
  mutate(returns_above_MAR = ifelse(returns > MAR,NA))

portfolio_returns_tq_rebalanced_monthly
})


portfolio_returns_tq_rebalanced_monthly <- eventReactive(input$go,{
  
  prices <- prices()
  w <- c(input$w1/100,input$w5/100)
  
  portfolio_returns_tq_rebalanced_monthly <- 
      prices %>% 
      to.monthly(indexAt = "last",-date) %>% 
      group_by(asset) %>%  
      mutate(returns = (log(returns) - log(lag(returns)))) %>%
  tq_portfolio(assets_col  = asset,weights     = w,col_rename  = "returns",rebalance_on = "months")
})

mean_port_return <- eventReactive(input$go,{
  
  portfolio_returns_tq_rebalanced_monthly <- portfolio_returns_tq_rebalanced_monthly()
  
  mean(portfolio_returns_tq_rebalanced_monthly$returns)
})

stddev_port_return <- eventReactive(input$go,{
  
  portfolio_returns_tq_rebalanced_monthly <- portfolio_returns_tq_rebalanced_monthly()
  
  sd(portfolio_returns_tq_rebalanced_monthly$returns)
})

simulation_accum_1 <- function(init_value,N,mean,stdev) {
    tibble(c(init_value,1 + rnorm(N,stdev))) %>% 
    `colnames<-`("returns") %>%
    mutate(growth = accumulate(returns,function(x,y) x * y)) %>% 
    select(growth)
}

sims <- eventReactive(input$go,{input$sims})

monte_carlo_sim_51 <- eventReactive(input$go,{ 
  
  sims <- sims()
  
  starts <-  
    rep(1,sims) %>%
    set_names(paste("sim",1:sims,sep = ""))
  
  map_dfc(starts,simulation_accum_1,N = input$sim_months,mean = mean_port_return(),stdev = stddev_port_return()) %>% 
  mutate(month = seq(1:nrow(.))) %>% 
  select(month,everything()) %>% 
  `colnames<-`(c("month",names(starts))) %>% 
  gather(sim,growth,-month) %>% 
  group_by(sim) %>% 
  mutate_all(funs(round(.,2)))
  
})

Portfel efektywny

行{data-height=600,.tabset}

最小差异投资组合权重

renderPlotly({
  validate(need(input$go != 0,"Please choose your portfolio assets,weights,MAR,rolling window and start date and click submit."))
  
  
  min_var <- min_var()
  
  min_var %>%
  gather(AAPL:TSLA,key = Asset,value = Weights) %>%
  mutate(Asset = as.factor(Asset)) %>%
  ggplot(aes(x = fct_reorder(Asset,Weights),y = Weights,fill = Asset)) +
  geom_bar(stat = 'identity') +
  theme_minimal() +
  labs(x = 'Assets',y = 'Weights',title = "Minimum Variance Portfolio Weights") +
  scale_y_continuous(labels = scales::percent)  
})

相切组合权重

renderHighchart({
  hchart( max_sr(),type = 'bar',hcaes(y = Weights,x = fct_reorder(Asset,group = Asset)) %>% 
  hc_title(text = paste(sims(),"Tangency Portfolio Weights",sep = " ")) %>%
  hc_xAxis(title = list(text = "Assets")) %>%
  hc_yAxis(title = list(text = "Weights"),labels = list(format = "${value}")) %>%
  hc_add_theme(hc_theme_flat()) %>%
  hc_exporting(enabled = TRUE) %>% 
  hc_legend(enabled = FALSE)
})

行{.tabset .tabset-fade}

投资组合优化和高效前沿

renderHighchart({
validate(need(input$go != 0,months,number of simulations and start date and click submit."))
  
  
})

输出

行{data-height=600,.tabset}

Symulacje

renderHighchart(
  hchart( monte_carlo_sim_51(),type = 'line',hcaes(y = growth,x = month,group = sim)) %>% 
  hc_title(text = paste(sims(),"Simulations",sep = " ")) %>%
  hc_xAxis(title = list(text = "months")) %>%
  hc_yAxis(title = list(text = "dollar growth"),labels = list(format = "${value}")) %>%
  hc_add_theme(hc_theme_flat()) %>%
  hc_exporting(enabled = TRUE) %>% 
  hc_legend(enabled = FALSE)
)

最小值、最大值、中值 Sim

renderHighchart({
validate(need(input$go != 0,number of simulations and start date and click submit."))
 
sim_summary <- 
  monte_carlo_sim_51() %>%
  summarise(final = last(growth)) %>% 
  summarise(
            max = max(final),min = min(final),median = median(final))

mc_max_med_min <- 
  monte_carlo_sim_51() %>%
  filter(
      last(growth) == sim_summary$max || 
      last(growth) == sim_summary$median ||
      last(growth) == sim_summary$min)

  hchart(mc_max_med_min,group = sim)) %>% 
  hc_title(text = "Min Max Median Simulations") %>%
  hc_xAxis(title = list(text = "months")) %>%
  hc_yAxis(title = list(text = "dollar growth"),labels = list(format = "${value}")) %>%
  hc_add_theme(hc_theme_flat()) %>%
  hc_exporting(enabled = TRUE) %>% 
  hc_legend(enabled = FALSE)
})

行{.tabset .tabset-fade}

直方图

renderPlot({
  validate(need(input$go != 0,rolling window and start date and click submit."))
  MAR <- mar()
  portfolio_returns_tq_rebalanced_monthly <- portfolio_returns_tq_rebalanced_monthly()
  
  portfolio_returns_tq_rebalanced_monthly %>% 
    ggplot(aes(x = returns)) +
    geom_histogram(alpha = 0.25,binwidth = .01,fill = "cornflowerblue") +
    geom_vline(xintercept = MAR,color = "green") +
    annotate(geom = "text",x = MAR,y = 10,label = "MAR",fontface = "plain",angle = 90,alpha = .5,vjust =  1)
})

密度

renderPlot({
  validate(need(input$go != 0,rolling window and start date and click submit."))
  MAR <- mar()
  portfolio_returns_tq_rebalanced_monthly <- portfolio_returns_tq_rebalanced_monthly()
  
  sortino_density_plot <- portfolio_returns_tq_rebalanced_monthly %>% 
    ggplot(aes(x = returns)) +
    stat_density(geom = "line",size = 1,color = "cornflowerblue") 
  
  shaded_area_data <- ggplot_build(sortino_density_plot)$data[[1]] %>% 
    filter(x < MAR)

  sortino_density_plot + 
  geom_area(data = shaded_area_data,aes(x = x,y = y),fill="pink",alpha = 0.5) +
  geom_segment(data = shaded_area_data,aes(x = MAR,y = 0,xend = MAR,yend = y),color = "red",linetype = "dotted") +
  annotate(geom = "text",y = 5,label = paste("MAR =",sep = ""),alpha = .8,vjust =  -1) +
  annotate(geom = "text",x = (MAR - .02),y = .1,label = "Downside",vjust =  -1)
           
})

滚动排序

renderHighchart({
  
validate(need(input$go != 0,rolling window and start date and click submit."))
  
  
  highchart(type = "stock") %>%
  hc_title(text = names(rolling_sortino())) %>%
  hc_add_series(rolling_sortino(),name = names(rolling_sortino()),color = "cornflowerblue") %>%
  hc_navigator(enabled = FALSE) %>% 
  hc_scrollbar(enabled = FALSE) 
 
})